Anisotropic local high-confidence voting for accurate stereo correspondence
Jiangbo Lu, Gauthier Lafruit, Francky Catthoor · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
We present a local area-based, discontinuity-preserving stereo matching algorithm that achieves high quality results near depth discontinuities as well as in homogeneous regions. To address the well-known challenge of defining appropriate support windows for local stereo methods, we use the anisotropic Local Polynomial Approximation (LPA) - Intersection of Confidence Intervals (ICI) technique. It can adaptively select a nearoptimal anisotropic local neighborhood for each pixel in the image. Leveraging this robust pixel-wise shape-adaptive support window, the proposed stereo method performs a novel matching cost aggregation step and an effective disparity refinement scheme entirely within a local high-confidence voting framework. Evaluation using the benchmark Middlebury stereo database shows that our method outperforms other local stereo methods, and it is even better than some algorithms using advanced but computationally complicated global optimization techniques.